An AI recognition method for children's clinical operative pain by skin potential (SP) signal
Objective and rationale: Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical appli...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024175895 |
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| author | Mingxuan Huang Cangcang Fu Linbo Chui Jiadong He Xiaozhi Wang Jikui Luo Bin Wu Yonggang Chen Shaohua Hu Jihua Zhu Yubo Li |
| author_facet | Mingxuan Huang Cangcang Fu Linbo Chui Jiadong He Xiaozhi Wang Jikui Luo Bin Wu Yonggang Chen Shaohua Hu Jihua Zhu Yubo Li |
| author_sort | Mingxuan Huang |
| collection | DOAJ |
| description | Objective and rationale: Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical applications. Method and results: This research established a pain recognition model based on AI skin potential (SP) signal analysis. A total of 237 subjects participated in this study, comprising 152 boys and 85 girls, ranging in age from 2 to 16 years old. Initially, we preprocessed SP signals and built datasets for pain and non-pain conditions, including 195 pain and 97 non-pain samples. Then, we applied wavelet transform (WT) to capture the time-frequency characteristics of the signals and extract energy features and created a feature set comprising 30 features and selected 10 most relevant ones using the “SelectKBest” function.We compared six algorithms, optimized their parameters, and evaluated the stability and fitting performance of each algorithm. The random forest (RF) algorithm emerged as the best, demonstrating significant performance in pain recognition with an accuracy of 80.3 % and a sensitivity of 92 %. The SP signals generated by children of different genders, ages, and needling positions during indwelling needle puncture were accurately recognized. Conclusion: We developed a comprehensive SP recognition model, innovatively employing WT for SP signal analysis. This time-frequency analysis method, by preserving low-frequency features, is particularly suitable for SP signals. By combining pain monitoring with SP signals and ML, subjective pain experiences are transformed into quantifiable data, achieving high accuracy and real-time measurement capabilities. These advantages provide valuable technical support for clinical pediatric pain management. |
| format | Article |
| id | doaj-art-3fcb03e8f50146eda11a7bf6f5460c6d |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-3fcb03e8f50146eda11a7bf6f5460c6d2025-01-17T04:51:38ZengElsevierHeliyon2405-84402025-01-01111e41558An AI recognition method for children's clinical operative pain by skin potential (SP) signalMingxuan Huang0Cangcang Fu1Linbo Chui2Jiadong He3Xiaozhi Wang4Jikui Luo5Bin Wu6Yonggang Chen7Shaohua Hu8Jihua Zhu9Yubo Li10College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, ChinaChildren's Hospital, Zhejiang University School of Medicine, Hangzhou, 310052, ChinaChildren's Hospital, Zhejiang University School of Medicine, Hangzhou, 310052, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China; International Joint Innovation Center, Zhejiang University, Haining, 314400, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China; International Joint Innovation Center, Zhejiang University, Haining, 314400, ChinaRuidiLab of Pulsed Power Medical Application, Hangzhou Ruidi Biotechnology Co.Ltd, Hangzhou, 310012, ChinaRuidiLab of Pulsed Power Medical Application, Hangzhou Ruidi Biotechnology Co.Ltd, Hangzhou, 310012, ChinaThe First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, ChinaChildren's Hospital, Zhejiang University School of Medicine, Hangzhou, 310052, China; National Clinical Research Center for Child Health, Hangzhou, 310052, China; Corresponding author. Children's Hospital, Zhejiang University School of Medicine, Hangzhou, 310052, China.College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China; International Joint Innovation Center, Zhejiang University, Haining, 314400, China; Corresponding author. Yuquan Campus, Zhejiang University, Hangzhou, Zhejiang, China.Objective and rationale: Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical applications. Method and results: This research established a pain recognition model based on AI skin potential (SP) signal analysis. A total of 237 subjects participated in this study, comprising 152 boys and 85 girls, ranging in age from 2 to 16 years old. Initially, we preprocessed SP signals and built datasets for pain and non-pain conditions, including 195 pain and 97 non-pain samples. Then, we applied wavelet transform (WT) to capture the time-frequency characteristics of the signals and extract energy features and created a feature set comprising 30 features and selected 10 most relevant ones using the “SelectKBest” function.We compared six algorithms, optimized their parameters, and evaluated the stability and fitting performance of each algorithm. The random forest (RF) algorithm emerged as the best, demonstrating significant performance in pain recognition with an accuracy of 80.3 % and a sensitivity of 92 %. The SP signals generated by children of different genders, ages, and needling positions during indwelling needle puncture were accurately recognized. Conclusion: We developed a comprehensive SP recognition model, innovatively employing WT for SP signal analysis. This time-frequency analysis method, by preserving low-frequency features, is particularly suitable for SP signals. By combining pain monitoring with SP signals and ML, subjective pain experiences are transformed into quantifiable data, achieving high accuracy and real-time measurement capabilities. These advantages provide valuable technical support for clinical pediatric pain management.http://www.sciencedirect.com/science/article/pii/S2405844024175895Wavelet transform (WT)Random forest (RF) algorithmSkin potential (SP) signalsPain recognitionMachine learning (ML) |
| spellingShingle | Mingxuan Huang Cangcang Fu Linbo Chui Jiadong He Xiaozhi Wang Jikui Luo Bin Wu Yonggang Chen Shaohua Hu Jihua Zhu Yubo Li An AI recognition method for children's clinical operative pain by skin potential (SP) signal Heliyon Wavelet transform (WT) Random forest (RF) algorithm Skin potential (SP) signals Pain recognition Machine learning (ML) |
| title | An AI recognition method for children's clinical operative pain by skin potential (SP) signal |
| title_full | An AI recognition method for children's clinical operative pain by skin potential (SP) signal |
| title_fullStr | An AI recognition method for children's clinical operative pain by skin potential (SP) signal |
| title_full_unstemmed | An AI recognition method for children's clinical operative pain by skin potential (SP) signal |
| title_short | An AI recognition method for children's clinical operative pain by skin potential (SP) signal |
| title_sort | ai recognition method for children s clinical operative pain by skin potential sp signal |
| topic | Wavelet transform (WT) Random forest (RF) algorithm Skin potential (SP) signals Pain recognition Machine learning (ML) |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024175895 |
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